"""
Logging & Monitoring Module
Author: AI Generated
Created: 2025-11-24
Purpose: Track pipeline performance, errors, and model drift
"""

import logging
from datetime import datetime
from typing import Dict, Any, Optional
import json
from pathlib import Path
import numpy as np

from database import db


# Configure logging
LOG_DIR = Path("logs")
LOG_DIR.mkdir(exist_ok=True)

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler(LOG_DIR / 'pipeline.log'),
        logging.StreamHandler()
    ]
)

logger = logging.getLogger(__name__)


class PipelineMonitor:
    """
    Monitor AI pipeline performance and log metrics.
    """
    
    def __init__(self):
        self.metrics_collection = "PipelineMetrics"
    
    def log_segmentation_run(self, metrics: Dict[str, Any]):
        """
        Log segmentation pipeline metrics.
        
        Metrics should include:
        - n_users: Number of users processed
        - n_segments: Number of segments created
        - inertia: K-means inertia
        - execution_time: Time in seconds
        - outliers_removed: Count
        """
        logger.info(f"Segmentation Run: {metrics}")
        
        # Save to MongoDB for trend analysis
        doc = {
            "pipeline": "segmentation",
            "timestamp": datetime.utcnow(),
            "metrics": metrics
        }
        db.get_collection(self.metrics_collection).insert_one(doc)
    
    def log_sentiment_run(self, metrics: Dict[str, Any]):
        """
        Log sentiment analysis metrics.
        
        Metrics should include:
        - n_comments: Number of comments analyzed
        - sentiment_distribution: {Positive: X, Negative: Y, Neutral: Z}
        - avg_confidence: Average confidence score
        - execution_time: Time in seconds
        """
        logger.info(f"Sentiment Analysis Run: {metrics}")
        
        doc = {
            "pipeline": "sentiment",
            "timestamp": datetime.utcnow(),
            "metrics": metrics
        }
        db.get_collection(self.metrics_collection).insert_one(doc)
    
    def log_genai_run(self, task: str, metrics: Dict[str, Any]):
        """
        Log Generative AI metrics.
        
        Metrics should include:
        - n_generated: Number of items generated
        - avg_generation_time: Average time per item
        - total_time: Total execution time
        """
        logger.info(f"GenAI Run ({task}): {metrics}")
        
        doc = {
            "pipeline": "genai",
            "task": task,
            "timestamp": datetime.utcnow(),
            "metrics": metrics
        }
        db.get_collection(self.metrics_collection).insert_one(doc)
    
    def log_error(self, pipeline: str, error: Exception, context: Dict = None):
        """
        Log pipeline errors.
        """
        logger.error(f"Error in {pipeline}: {str(error)}", exc_info=True)
        
        doc = {
            "pipeline": pipeline,
            "timestamp": datetime.utcnow(),
            "error": str(error),
            "error_type": type(error).__name__,
            "context": context or {}
        }
        db.get_collection("PipelineErrors").insert_one(doc)
    
    def detect_drift_segmentation(self, current_centroids: np.ndarray) -> Dict:
        """
        Detect drift in K-means clustering.
        Compare current centroids with previous run.
        """
        # Fetch last run's centroids
        last_metric = db.get_collection(self.metrics_collection).find_one(
            {"pipeline": "segmentation"},
            sort=[("timestamp", -1)]
        )
        
        if not last_metric or "centroids" not in last_metric["metrics"]:
            logger.info("No previous centroids found for drift detection")
            return {"drift_detected": False, "reason": "no_baseline"}
        
        # Calculate drift as Euclidean distance between centroids
        prev_centroids = np.array(last_metric["metrics"]["centroids"])
        
        if prev_centroids.shape != current_centroids.shape:
            return {"drift_detected": True, "reason": "shape_mismatch"}
        
        # Calculate average distance
        distances = np.linalg.norm(current_centroids - prev_centroids, axis=1)
        avg_drift = float(np.mean(distances))
        max_drift = float(np.max(distances))
        
        # Threshold: if average drift > 0.5 std, flag as drift
        drift_detected = avg_drift > 0.5
        
        result = {
            "drift_detected": drift_detected,
            "avg_drift": avg_drift,
            "max_drift": max_drift,
            "threshold": 0.5
        }
        
        if drift_detected:
            logger.warning(f"⚠️ Cluster drift detected: avg={avg_drift:.3f}, max={max_drift:.3f}")
        
        return result
    
    def detect_drift_sentiment(self, current_distribution: Dict[str, int]) -> Dict:
        """
        Detect drift in sentiment distribution.
        """
        # Fetch last run's distribution
        last_metric = db.get_collection(self.metrics_collection).find_one(
            {"pipeline": "sentiment"},
            sort=[("timestamp", -1)]
        )
        
        if not last_metric:
            return {"drift_detected": False, "reason": "no_baseline"}
        
        prev_dist = last_metric["metrics"].get("sentiment_distribution", {})
        
        # Calculate total counts
        prev_total = sum(prev_dist.values())
        curr_total = sum(current_distribution.values())
        
        if prev_total == 0 or curr_total == 0:
            return {"drift_detected": False, "reason": "insufficient_data"}
        
        # Calculate percentage change for each sentiment
        changes = {}
        for label in ["Positive", "Negative", "Neutral"]:
            prev_pct = prev_dist.get(label, 0) / prev_total
            curr_pct = current_distribution.get(label, 0) / curr_total
            changes[label] = abs(curr_pct - prev_pct)
        
        # Drift if any sentiment changes > 10%
        max_change = max(changes.values())
        drift_detected = max_change > 0.1
        
        result = {
            "drift_detected": drift_detected,
            "changes": changes,
            "max_change": max_change,
            "threshold": 0.1
        }
        
        if drift_detected:
            logger.warning(f"⚠️ Sentiment drift detected: max_change={max_change:.1%}")
        
        return result
    
    def get_performance_summary(self, pipeline: str, days: int = 7) -> Dict:
        """
        Get performance summary for the last N days.
        """
        from datetime import timedelta
        
        cutoff = datetime.utcnow() - timedelta(days=days)
        
        metrics = list(db.get_collection(self.metrics_collection).find({
            "pipeline": pipeline,
            "timestamp": {"$gte": cutoff}
        }).sort("timestamp", -1))
        
        if not metrics:
            return {"error": "No metrics found"}
        
        # Aggregate
        total_runs = len(metrics)
        avg_time = np.mean([m["metrics"].get("execution_time", 0) for m in metrics])
        
        return {
            "pipeline": pipeline,
            "period_days": days,
            "total_runs": total_runs,
            "avg_execution_time": avg_time,
            "last_run": metrics[0]["timestamp"]
        }


# Global monitor instance
monitor = PipelineMonitor()